A Security Framework for Chemical Functions

2026-01-20

AI Summary

This work presents the first formal security model and evaluation methodology for physical unclonable functions (PUFs) based on chemical systems. We model such systems as noisy challenge-response primitives and establish a unified security framework that rigorously defines robustness, unclonability, and unpredictability through adversarial security games. Our approach integrates PUF theory with maximum-likelihood verification, binomial parameter estimation, sequencing noise modeling, partial-edit models, and standard key extraction techniques. Quantitative analysis on two DNA-based constructions yields provable security bounds, successfully enabling in-product authentication and shared secret generation. The proposed methodology offers a reproducible foundation for the design and validation of chemical PUFs.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In this paper, we introduce chemical functions, a unified framework that models chemical systems as noisy challenge--response primitives, and formalize the associated chemical function infrastructure. Building on the theory of physical functions, we rigorously define robustness, unclonability, and unpredictability for chemical functions in both finite and asymptotic regimes, and specify security games that capture the adversary's power and the security goals. We instantiate the framework with two existing DNA-based constructions (operable random DNA and Genomic Sequence Encryption) and derive quantitative bounds for robustness, unclonability, and unpredictability. Our analysis develops maximum-likelihood verification rules under sequencing noise and partial-edit models, and provides high-precision estimates based on binomial distributions to guide parameter selection. The framework, definitions, and analyses yield a reproducible methodology for designing chemically unclonable authentication mechanisms. We demonstrate applications to in-product authentication and to shared key generation using standard extraction techniques.

Problem

• chemical functions
• unclonability
• robustness
• unpredictability
• security framework

Innovation

• chemical functions
• unclonability
• challenge-response primitives
• DNA-based security
• physical unclonable functions